{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T20:30:04Z","timestamp":1764880204892,"version":"build-2065373602"},"reference-count":23,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,20]],"date-time":"2022-03-20T00:00:00Z","timestamp":1647734400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Temperature field calculation is an important step in infrared image simulation. However, the existing solutions, such as heat conduction modelling and pre-generated lookup tables based on temperature calculation tools, are difficult to meet the requirements of high-performance simulation of infrared images based on three-dimensional scenes under multi-environmental conditions in terms of accuracy, timeliness, and flexibility. In recent years, machine learning-based temperature field prediction methods have been proposed, but these methods only consider the influence of meteorological parameters on the temperature value, while not considering the geometric structure and the thermophysical parameters of the object, which results in the low accuracy. In this paper, a multivariate temperature field prediction network based on heterogeneous data (MTPHNet) is proposed. The network fuses geometry structure, meteorological, and thermophysical parameters to predict temperature. First, a Point Cloud Feature Extraction Module and Environmental Data Mapping Module are used to extract geometric information, thermophysical, and meteorological features. The extracted features are fused by the Data Fusion Module for temperature field prediction. Experiment results show that MTPHNet significantly improves the prediction accuracy of the temperature field. Compared with the v-Support Vector Regression and the combined back-propagation neural network, the mean absolute error and root mean square error of MTPHNet are reduced by at least 23.4% and 27.7%, respectively, while the R-square is increased by at least 5.85%. MTPHNet also achieves good results in multi-target and complex target temperature field prediction tasks. These results validate the effectiveness of the proposed method.<\/jats:p>","DOI":"10.3390\/s22062386","type":"journal-article","created":{"date-parts":[[2022,3,20]],"date-time":"2022-03-20T21:37:17Z","timestamp":1647812237000},"page":"2386","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Amalgamation of Geometry Structure, Meteorological and Thermophysical Parameters for Intelligent Prediction of Temperature Fields in 3D Scenes"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3827-371X","authenticated-orcid":false,"given":"Yuan","family":"Cao","sequence":"first","affiliation":[{"name":"National Space Science Center, Key Laboratory of Electronics and Information Technology for Space System, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ligang","family":"Li","sequence":"additional","affiliation":[{"name":"National Space Science Center, Key Laboratory of Electronics and Information Technology for Space System, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Ni","sequence":"additional","affiliation":[{"name":"National Space Science Center, Key Laboratory of Electronics and Information Technology for Space System, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7532-2384","authenticated-orcid":false,"given":"Bo","family":"Liu","sequence":"additional","affiliation":[{"name":"National Space Science Center, Key Laboratory of Electronics and Information Technology for Space System, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenbo","family":"Zhou","sequence":"additional","affiliation":[{"name":"National Space Science Center, Key Laboratory of Electronics and Information Technology for Space System, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Xiao","sequence":"additional","affiliation":[{"name":"National Space Science Center, Key Laboratory of Electronics and Information Technology for Space System, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"22344","DOI":"10.1364\/OE.20.022344","article-title":"Remote detection of buried land-mines and IEDs using LWIR polarimetric imaging","volume":"20","author":"Gurton","year":"2012","journal-title":"Opt. Express"},{"key":"ref_2","unstructured":"Jacobs, P.A.M. (1980). Simulation of the Thermal Behaviour of an Object and Its Nearby Surroundings, TNO Publication. TNO-Report PH."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1117\/12.940535","article-title":"Real-Time Simulated Forward Looking Infrared (FLIR) Imagery For Training","volume":"Volume 781","author":"Biesel","year":"1987","journal-title":"Infrared Image Processing and Enhancement"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Curtis, J.O., and Rivera, J.S. (1990). Diurnal and seasonal variation of structural element thermal signatures. Proceedings of the SPIE 1311, Characterization, Propagation, and Simulation of Infrared Scenes, International Society for Optics and Photonics.","DOI":"10.1117\/12.21828"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Balfour, L.S., and Bushlin, Y. (1997). Semi-empirical model-based approach for IR scene simulation. Proceedings of the SPIE 3061, Infrared Technology and Applications XXIII, International Society for Optics and Photonics.","DOI":"10.1117\/12.280381"},{"key":"ref_6","unstructured":"Gonda, T.G., Jones, J.C., Gerhart, G.R., Thomas, D.J., Martin, G.L., and Sass, D.T. (1988, January 4\u20136). PRISM Based Thermal Signature Modeling Simulation. Proceedings of the SPIE Symposium on IR Sensors and Sensor Fusion, Orlando, FL, USA."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Sheffer, A.D., and Cathcart, J.M. (1988). Computer generated IR imagery: A first principles modeling approach. Proceedings of the SPIE 0933, Multispectral Image Processing and Enhancement, International Society for Optics and Photonics.","DOI":"10.1117\/12.968469"},{"key":"ref_8","first-page":"73000J","article-title":"Validation of the thermal code of RadTherm-IR, IR-Workbench, and F-TOM","volume":"7300","author":"Schwenger","year":"2009","journal-title":"SPIE Def. Secur. Sens."},{"key":"ref_9","first-page":"329\u2013333+344","article-title":"Real Time Infrared Scene Simulation System Based on Database Lookup Table Technology","volume":"6","author":"Hu","year":"2013","journal-title":"Infrared Technol."},{"key":"ref_10","first-page":"272","article-title":"Infrared Signature Simulation Based on the Modular Neural Network","volume":"4","author":"Huang","year":"2006","journal-title":"J. Proj. Rocket. Missiles Guid."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1143","DOI":"10.1007\/s10762-007-9270-4","article-title":"Infrared image simulation based on statistical learning theory","volume":"28","author":"Huang","year":"2007","journal-title":"Int. J. Infrared. Millim. Waves"},{"key":"ref_12","first-page":"155","article-title":"Support vector regression machines","volume":"9","author":"Drucker","year":"1997","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"ref_13","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017). Attention is all you need. Adv. Neural Inf. Process. Syst., 5998\u20136008."},{"key":"ref_14","first-page":"2204","article-title":"Recurrent models of visual attention","volume":"2","author":"Mnih","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4058","DOI":"10.1364\/OE.447885","article-title":"Absorption and scattering in perfect ther-mal radiation absorber-emitter metasurfaces","volume":"30","author":"Nishijima","year":"2022","journal-title":"Opt. Express"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., and Zhang, W. (2021, January 2\u20139). Informer: Beyond efficient Transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, Palo Alto, CA USA.","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"ref_17","unstructured":"Qi, C.R., Su, H., Kaichun, M., and Guibas, L.J. (2017, January 21\u201326). Pointnet: Deep learning on point sets for 3D classification and segmenta-tion. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA."},{"key":"ref_18","unstructured":"Qi, C.R., Yi, L., Su, H., and Guibas, L.J. (2017). Pointnet++: Deep hi-erarchical feature learning on point sets in a metric space. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","article-title":"Backpropagation Applied to Handwritten Zip Code Recognition","volume":"1","author":"LeCun","year":"1989","journal-title":"Neural Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"115727","DOI":"10.1016\/j.eswa.2021.115727","article-title":"Fast prediction of complicated temperature field using Conditional Mul-ti-Attention Generative Adversarial Networks (CMAGAN)","volume":"186","author":"Chen","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"79","DOI":"10.3354\/cr030079","article-title":"Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance","volume":"30","author":"Willmott","year":"2005","journal-title":"Clim. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1016\/j.ijforecast.2006.03.001","article-title":"Another look at measures of forecast accuracy","volume":"22","author":"Hyndman","year":"2006","journal-title":"Int. J. Forecast."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e623","DOI":"10.7717\/peerj-cs.623","article-title":"The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation","volume":"7","author":"Chicco","year":"2021","journal-title":"PeerJ Comput. Sci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/6\/2386\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:39:48Z","timestamp":1760135988000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/6\/2386"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,20]]},"references-count":23,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["s22062386"],"URL":"https:\/\/doi.org\/10.3390\/s22062386","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,3,20]]}}}